Study of radiochemotherapy decision-making for young high-risk low-grade glioma patients using a macroscopic and microscopic combined radiomics model

Guoqing Wu; Zhifeng Shi; Zeyang Li; Xuan Xie; Qisheng Tang; Jingjing Zhu; Zhong Yang; Yuanyuan Wang; Jinsong Wu; Jinhua Yu

doi:10.1007/s00330-023-10378-9

Study of radiochemotherapy decision-making for young high-risk low-grade glioma patients using a macroscopic and microscopic combined radiomics model

Eur Radiol. 2023 Oct 27. doi: 10.1007/s00330-023-10378-9. Online ahead of print.

Authors

Guoqing Wu^#^{1

2}, Zhifeng Shi^#^{3

4

5

6}, Zeyang Li^{3

4

5

6}, Xuan Xie^{1

2}, Qisheng Tang^{3

4

5

6}, Jingjing Zhu⁷, Zhong Yang⁸, Yuanyuan Wang^{1

2}, Jinsong Wu^{9

10

11

12}, Jinhua Yu^{13

14

15}

Affiliations

¹ School of Information Science and Technology, Fudan University, Shanghai, China.
² Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, China.
³ Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
⁴ National Center for Neurological Disorders, Shanghai, China.
⁵ Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China.
⁶ Neurosurgical Institute of Fudan University, Shanghai, China.
⁷ Department of Pathology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
⁸ Department of Radiology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
⁹ Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China. wujinsong@huashan.org.cn.
¹⁰ National Center for Neurological Disorders, Shanghai, China. wujinsong@huashan.org.cn.
¹¹ Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China. wujinsong@huashan.org.cn.
¹² Neurosurgical Institute of Fudan University, Shanghai, China. wujinsong@huashan.org.cn.
¹³ School of Information Science and Technology, Fudan University, Shanghai, China. jhyu@fudan.edu.cn.
¹⁴ Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, China. jhyu@fudan.edu.cn.
¹⁵ The AI Lab of Huashan Hospital, Fudan University, Shanghai, China. jhyu@fudan.edu.cn.

^# Contributed equally.

PMID: 37889272
DOI: 10.1007/s00330-023-10378-9

Abstract

Objectives: As a few types of glioma, young high-risk low-grade gliomas (HRLGGs) have higher requirements for postoperative quality of life. Although adjuvant chemotherapy with delayed radiotherapy is the first treatment strategy for HRLGGs, not all HRLGGs benefit from it. Accurate assessment of chemosensitivity in HRLGGs is vital for making treatment choices. This study developed a multimodal fusion radiomics (MFR) model to support radiochemotherapy decision-making for HRLGGs.

Methods: A MFR model combining macroscopic MRI and microscopic pathological images was proposed. Multiscale features including macroscopic tumor structure and microscopic histological layer and nuclear information were grabbed by unique paradigm, respectively. Then, these features were adaptively incorporated into the MFR model through attention mechanism to predict the chemosensitivity of temozolomide (TMZ) by means of objective response rate and progression free survival (PFS).

Results: Macroscopic tumor texture complexity and microscopic nuclear size showed significant statistical differences (p < 0.001) between sensitivity and insensitivity groups. The MFR model achieved stable prediction results, with an area under the curve of 0.950 (95% CI: 0.942-0.958), sensitivity of 0.833 (95% CI: 0.780-0.848), specificity of 0.929 (95% CI: 0.914-0.936), positive predictive value of 0.833 (95% CI: 0.811-0.860), and negative predictive value of 0.929 (95% CI: 0.914-0.934). The predictive efficacy of MFR was significantly higher than that of the reported molecular markers (p < 0.001). MFR was also demonstrated to be a predictor of PFS.

Conclusions: A MFR model including radiomics and pathological features predicts accurately the response postoperative TMZ treatment.

Clinical relevance statement: Our MFR model could identify young high-risk low-grade glioma patients who can have the most benefit from postoperative upfront temozolomide (TMZ) treatment.

Key points: • Multimodal radiomics is proposed to support the radiochemotherapy of glioma. • Some macro and micro image markers related to tumor chemotherapy sensitivity are revealed. • The proposed model surpasses reported molecular markers, with a promising area under the curve (AUC) of 0.95.

Keywords: Hematoxylin and eosin-stained slides; Magnetic resonance imaging; Multimodal radiomics; Radiochemotherapy; Young high-risk low-grade glioma patients.

Abstract

Grants and funding